Application of abnormal event detection technology to olefins recovery trains

Abstract

The present invention is a method for detecting an abnormal event for process units of an ethylene processing system. The method compares the operation of the process units to a model developed by principal components analysis of normal operation for these units. If the difference between the operation of a process unit and the normal operation indicates an abnormal condition, then the cause of the abnormal condition is determined and corrected.

1. A method for abnormal event detection (AED) for current operation for some process units of an ethylene processing system comprising:
 (a) comparing online measurements from the process units to a set of models including principal component analysis models and/or engineering models for normal operation of the corresponding process units, wherein said ethylene processing system has been divided into equipment groups and process operating modes wherein there is minimal interaction between said equipment groups and principal component analysis models correspond to equipment groups and process operating modes, 
 (b) determining if the current operation differs from expected normal operations so as to indicate the presence of an abnormal condition in a process unit, and 
 (c) determining the underlying cause of an abnormal condition in the ethylene processing system. 
 
     
     
       2. The method of  claim 1  wherein said set of models corresponds to equipment groups and process operating modes, one model for each group which may include one or more operating modes. 
     
     
       3. The method of  claim 1  wherein said set of models correspond to equipment groups and process operating modes, one model for each group and each mode. 
     
     
       4. The method of  claim 1  wherein said set of models of normal operations further includes engineering models. 
     
     
       5. The method of  claim 1  wherein said ethylene processing system is divided into operational areas with a principal component analysis model for each area. 
     
     
       6. The method of  claim 1  further comprising the steps of
 (a) dividing the ethylene process into equipment groups with one or more operating modes, 
 (b) determining input variables and their operating range for said equipment groups and/or operating modes, 
 (c) determining historical training and testing data for said input variables, 
 (d) creating a set of principle component analysis models and/or engineering models representing normal operations, where each model corresponds to one of said equipment groups including one or more operating modes, 
 (e) dividing the ethylene process into operational areas where a set of one or more models corresponds an operational area, and 
 (f) comparing online measurements from the ethylene process units to a set of models for normal operation of the corresponding operational area. 
 
     
     
       7. The method of  claim 1  wherein said equipment groups have minimal interactions among each other. 
     
     
       8. The method of  claim 1  wherein said equipment groups include feedforward information from upstream equipment groups and feedback information from downstream equipment groups. 
     
     
       9. The method of  claim 1  wherein inputs to the models are measurements of the performance and/or physical state of the ethylene process, and/or control signals sent to field actuators from the control system, and/or external factors affecting the ethylene process. 
     
     
       10. The method of  claim 1  wherein the inputs to the models determine the physical state of the ethylene process. 
     
     
       11. The method of  claim 1  wherein said indication of an abnormal event further comprises of suppressing model calculations to eliminate false positives on special cause operations. 
     
     
       12. The method of  claim 1  wherein unwanted abnormal event indications are suppressed using override suppressors. 
     
     
       13. The method of  claim 2  wherein said equipment groups include all major material and energy interactions in the same group. 
     
     
       14. The method of  claim 2  wherein each said model further comprises suppressing model calculations to eliminate false positives on special cause operations. 
     
     
       15. The method of  claim 2  further comprising the steps of(a)-determining said each model begins with a rough model based on questionable data, (b) using said rough model to gather high quality training data, and improve the model, and (e) repeating step (b) to further improve the model. 
     
     
       16. The method of  claim 2  wherein said model includes transformed variables. 
     
     
       17. The method of  claim 2  wherein principal components include process variables provided by online measurements. 
     
     
       18. The method of  claim 4  wherein said engineering model further comprises determining consistency between some control valves and flow meters. 
     
     
       19. The method of  claim 4  wherein the principle components for the principle component analysis models are selected based on named degrees of freedom in the equipment group. 
     
     
       20. The method of  claim 4  wherein the number of principal components is chosen such that coefficients of the non-chosen principal components become about equal in size. 
     
     
       21. The method of  claim 5  wherein there are eleven operational areas. 
     
     
       22. The method of  claim 5  wherein said principal components analysis models include process variable values measured by sensors. 
     
     
       23. The method of  claim 5  wherein said principal components analysis models for different process units include some process variable values measured by the same sensor. 
     
     
       24. The method of  claim 5  wherein a number of principal components is selected by a magnitude of total process variation represented by successive components. 
     
     
       25. The method of  claim 6  wherein operating modes are specific time periods wherein the ethylene process behavior is significantly different. 
     
     
       26. The method of  claim 6  wherein historical data (raining sets arc segregated according to operating modes. 
     
     
       27. The method of  claim 6  wherein said models includes transformed variables. 
     
     
       28. The method of  claim 6  further comprising the steps
 (a) determining said model begins with a rough model based on questionable data, 
 (b) using said rough model to gather high quality training data, and improve the model, and 
 (c) repeating step (b) to further improve the model. 
 
     
     
       29. The method of  claim 6  wherein a set of models of normal operations includes one or more Principle Component Analysis Models and/or one or more engineering models. 
     
     
       30. The method of  claim 6  wherein the number of principle components is selected by the magnitude of total process variation represented by successive principle components. 
     
     
       31. The method of  claim 6  wherein said principal component analysis models for different equipment groups include as inputs some process variable values measured by the same sensor. 
     
     
       32. The method of  claim 10  wherein said inputs to the models include temperatures, and/or pressures, and/or flows, and/or analyzer readings, and/or vibrations, and/or electrical current, and/or signals to field actuators. 
     
     
       33. The method of  claim 12  wherein override suppressors act automatically whenever a defined condition exists. 
     
     
       34. The method of  claim 12  wherein the operator initiates the action of an override suppressor. 
     
     
       35. The method of  claim 13  wherein said equipment groups include quick recycles in the same group. 
     
     
       36. The method of  claim 15  wherein said training data includes historical data for the model of the processing unit. 
     
     
       37. The method of  claim 16  wherein said transformed variables include reflux to feed ratio in distillation columns, log of composition in high purity distillation, pressure compensated temperature measurement, sidestream yield, flow to valve position, and reaction rate to exponential temperature change. 
     
     
       38. The method of  claim 16  wherein some pairs of measurements for two variables are brought into time synchronization by one of the variables using a dynamic transfer function. 
     
     
       39. The method of  claim 16  wherein variables of process measurements that are affected by operating point changes in process operations are converted to deviation variables. 
     
     
       40. The method of  claim 16  wherein said model inputs are corrected for noise. 
     
     
       41. The method of  claim 16  wherein the measurements of a variable are scaled. 
     
     
       42. The method of  claim 16  wherein measurements are transformed to bring tern into time synchronization, with one of the other variables using a dynamic transfer function. 
     
     
       43. The method of  claim 17  wherein principal components include some measurement pairs that are time synchronized to one of the variables using a dynamic filter. 
     
     
       44. The method of  claim 17  wherein process variables affected by operating point changes in the process operations are convened to deviation variables. 
     
     
       45. The method of  claim 21  wherein the eleven operational areas of the ethylene processing system include; charge gas compressor; drier, cold box, and methane booster; ethylene and propylene refrigeration; demethanizer; deethanizer; acetylene converter; C2 splitter; methanator and H2 drier; depropanizer; MAPD hydrofiner; and propylene rerun and fractionator. 
     
     
       46. The method of  claim 27  wherein said transformed variables include reflux to feed ratio in distillation columns, log of composition in high purity distillation, pressure compensated temperature measurement, sidestream yield, flow to valve position, and reaction rate to exponential temperature change. 
     
     
       47. The method of  claim 28  wherein said training data includes historical data for the model of the processing unit. 
     
     
       48. The method of  claim 36  wherein the number of principal components is chosen such that coefficients of each principal component become about equal m size. 
     
     
       49. The method of  claim 36  wherein the historical data periods selected for training and testing data sets are selected to increase cross correlation among inputs and/or increase the signal to noise ratio of the inputs in the data. 
     
     
       50. The method of  claim 39  wherein a rolling average of inputs from recent operations is subtracted from the current value of the measurements. 
     
     
       51. The method of  claim 40  wherein said model inputs are corrected by filtering or eliminating noisy measurements of variables. 
     
     
       52. The method of  claim 41  wherein the measurements are scaled to the expected normal range of that variable. 
     
     
       53. The method of  claim 49  wherein data from time periods of steady operations are eliminated from the training data. 
     
     
       54. The method of  claim 49  wherein data from severe abnormal operations are eliminated from the training data. 
     
     
       55. The method of  claim 49  wherein potential model inputs are deleted from the inputs used in the model. 
     
     
       56. A system for abnormal event detection (AED) for some process units of an ethylene processing system comprising:
 (a) on-line data acquisition of measurements and control signals, 
 (b) a set of models including principal component models and/or engineering models included in the set describing operations of the process units of said ethylene processing system, wherein said ethylene process system has been divided into equipment groups, wherein said set of models includes at least a principal component model corresponding to an equipment group, 
 (c) a set of displays which indicate if the current operation differs from expected normal operations so as to indicate the presence of an abnormal condition in the process unit, and 
 (d) a set of displays which indicates the underlying cause of an abnormal condition in the ethylene processing system. 
 
     
     
       57. The system of  claim 56  wherein said model for each process unit is either a principal components model or an engineering model. 
     
     
       58. The system of  claim 56  wherein inputs to the system are measurements of the performance and/or physical state of the ethylene process, and/or control signals sent to process field devices from the control system, and/or external factors affecting the ethylene process. 
     
     
       59. The system of  claim 56  wherein said models further comprises suppression calculations to eliminate unwanted notifications of known conditions. 
     
     
       60. The system of  claim 56  wherein an ethylene processing system is partitioned into operational sections and the models are grouped into sets according to their associated operational section. 
     
     
       61. The system of  claim 56  wherein the operator interface includes a continuous indication of the likelihood of an abnormality within each operational section of the ethylene processing system. 
     
     
       62. The system of  claim 56  wherein the operator interface allows the operator to navigate to a display associated with the highest likelihood of an abnormal condition with a single mouse click. 
     
     
       63. The system of  claim 57  wherein an ethylene processing system is partitioned into operational sections with a principal components model for each section. 
     
     
       64. The system of  claim 57  wherein said principal components include process variables provided by online measurements. 
     
     
       65. The system of  claim 57  wherein said model further comprises calculations to eliminate operator induced notifications and false positives. 
     
     
       66. The system of  claim 57  wherein said model includes transformed variables. 
     
     
       67. The system of  claim 58  wherein said inputs to the system include temperatures, and/or pressures, and/or flows, and/or analyzer readings, and/or vibrations, and/or electrical current, and/or signals to field actuators. 
     
     
       68. The system of  claim 58  wherein said inputs to the system become transformed inputs to the models. 
     
     
       69. A system of  claim 59  wherein override suppressors act automatically whenever a defined condition exists. 
     
     
       70. A system of  claim 59  wherein the operator initiates the action of an override suppressor. 
     
     
       71. The system of  claim 61  wherein the likelihood of an abnormality within an operational section is indicated to the operator with a trend of the highest likelihood of an abnormality. 
     
     
       72. The system of  claim 61  wherein the operator is alerted whenever the likelihood of an abnormality exceeds a defined threshold. 
     
     
       73. The system of  claim 62  wherein said displays include Pareto charts. 
     
     
       74. The system of  claim 62  wherein, for a set of models, the Pareto charts list models in the order of magnitude of the likelihood of an abnormal condition as indicated by each model. 
     
     
       75. The system of  claim 62  wherein said displays include X-Y plots. 
     
     
       76. The system of  claim 63  wherein there are eleven operational sections. 
     
     
       77. The system of  claim 63  wherein fuzzy Petri nets are used to convert outputs from models to the likelihood of an abnormal process operation and/or equipment problem. 
     
     
       78. The system of  claim 66  wherein said transformed variables include reflux to total product flow in distillation columns, log of composition and overhead pressure in distillation columns, pressure compensated temperature measurements, flow to valve position and bed differential temperature and pressure. 
     
     
       79. The system of  claim 66  wherein some measurement pairs are time synchronized to one of the variables using a dynamic filter. 
     
     
       80. The system of  claim 66  wherein process measurement variables affected by operating point changes in the process operations are converted to deviation variables. 
     
     
       81. The system of  claim 66  wherein the measurements and transforms of a variable are scaled prior to model identification. 
     
     
       82. The system of  claim 66  wherein a number of principal components is selected by a magnitude of total process variation represented by successive components. 
     
     
       83. The system of  claim 66  wherein noise is removed from the system inputs and/or the transformed variables. 
     
     
       84. The system of  claim 66  wherein the measurements of a variable are scaled prior to model calculation. 
     
     
       85. The system of  claim 73  wherein, for principle component analysis models, the Pareto charts show in order of magnitude the specific contribution of each input to the estimate of the likelihood of an abnormal condition. 
     
     
       86. The system of  claim 75  wherein, two dimensional models are shown as X-Y plots. 
     
     
       87. The system of  claim 75  wherein, the X-Y plot shows the estimate of the normal operating relationship as well as the confidence region around that estimate. 
     
     
       88. The system of  claim 75  wherein the operator can recalibrate the X-Y plot on demand. 
     
     
       89. The system of  claim 75  wherein time adjacent data sample points are connected. 
     
     
       90. The system of  claim 75  wherein the normal process operating range of the X axis variable is indicated. 
     
     
       91. The system of  claim 77  wherein the likelihood of abnormality indicated by models within a set are combined using fuzzy Petri nets to estimate the likelihood of an abnormality within an operational section. 
     
     
       92. The system of  claim 80  wherein the measurements are scaled by the expected normal range of that transformed and/or deviation variable. 
     
     
       93. The system of  claim 83  wherein noise is removed using digital filters. 
     
     
       94. The system of  claim 83  wherein noise is removed using logic. 
     
     
       95. The system of  claim 85  wherein the operator can navigate to time plots of inputs whose order is determined by each input's position on the Pareto chart.